Spatial modelling

1. Spatial Analysis & Modeling
GIS for Spatial Planning
Training for Ministry of Transport
Mozambique
Maputo, Mozambique
2-13 July 2018
Geoinformation and Sectoral Statistics Section

2.  Spatial Analysis and Modeling Concepts
 Surface Models
 Surface Modeling Operations
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Content

3.  Spatial analysis is the technique to analyze data in terms of
location
 Find out patterns, identify relationships among features,
plan efficient routes, perform site selection, model or
predict values based on discrete sample observations, etc.
 Relationships: proximity, overlap, intersection, visibility,
accessibility, etc.
 Detect patterns: hotspots, outliers, clusters, change over
time
Spatial Analysis & Modeling Concepts

4.  Predict values: given a set of measured points
across an area you can determine the estimated
values in unmeasured locations
 Using spatial analysis, you can combine
information from many independent sources and
derive a new set of information by applying a large,
rich, and sophisticated set of spatial operators or
geo-processing tools
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Spatial Analysis & Modeling Concepts

5.  Why Spatial modeling: finding relationships among
geographic features to understand and address any
particular problem
 Spatial modeling allows you to derive new data from values
of existing data layers and to predict what might happen
and where
 Modeling often involve developing specialized workflows
through programming, creating scripts and automated
workflows, lets you efficiently query and process large
amounts of data and implement more complex algorithms.
Spatial Analysis & Modeling Concepts

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 Models help you understand, describe, and predict how things
work in the real world
 Spatial modeling, modeling spatial problems, involve two types
of models:
 Representation models – represent the objects in the landscape
 Process models – simulate the processes in the landscape
Spatial Analysis & Modeling Concepts

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Vector model
Raster model
Spatial Analysis & Modeling Concepts
Spatial Analysis

8.  Using an elevation surface, for example, you can
derive information and identify features that were
not readily apparent in the original surface,
 contours, angle of slope, steepest downslope direction
(aspect), shaded relief (hillshade), and visible areas
(viewsheds)
 model the flow of water across Earth’s surface,
deriving runoff characteristics, understanding
drainage systems, and creating watersheds
Spatial Analysis & Modeling Concepts

9.  Surface Models: DSM, DEM, DTM
 Digital Surface Model (DSM) surface model which captures
the natural and built features on the Earth’s surface
 DSM is generated using LiDAR system, which sends pulses of
light to the ground and when the pulse of light bounces
off/back its target and returns to the sensor, it gives the range
(a variable distance) to the Earth
 LiDAR delivers a massive point cloud filled of varying elevation
values (Height can come from the top of buildings, tree
canopy, power lines, other built and natural features)
 DSM is useful in 3D modeling for telecommunications, urban
planning and aviation (objects extrude from the earth,
particularly useful in these application to identify obstructions)
Surface Models

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Surface Models
DSM

11.  Digital Elevation Model (DEM) is a digital model or 3D
representation of a terrain’s surface, created from DEM is
bare-earth raster grid, which filters out vegetation and man
made features terrain elevation data
 DEM is bare-earth raster grid, which filters out vegetation and
man made features
 non-ground points such as bridges and roads, built (power
lines, buildings and towers), and natural (trees and other
vegetation types) are not included in DEM
 Digital Elevation Model (DEM) is frequently used and simplest
form of digital representation of topography
Surface Models

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 DEM is used to determine
terrain attributes such as
elevation, slope and aspect
 Terrain features such as
drainage basins and
watersheds, drainage
networks and channel can
be identified from DEMs.
 Widely used in hydrologic
modeling, and geologic
analysis, soil mapping
Surface Models

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Surface Models
 A Digital Terrain Model (DTM) is a vector data set composed
of regularly spaced points and natural features such as ridges
and breaklines.
 DTMs are typically
created through stereo
photogrammetry
 From these regularly-
space and contour lines,
you can interpolate a
DTM into a DEM

14.  Surface modeling operations include:
 Contour
 Slope
 Aspect
 Hillshade
 Viewshed
 Visibility
 Cut Fill
 Interpolation
Surface Modeling Operations

15. Aspect (Spatial Analyst)
 Derives aspect from a raster surface. The aspect identifies the
downslope direction of the maximum rate of change in value from each
cell to its neighbors.
 Aspect can be thought of as the slope direction. The values of the
output raster will be the compass direction of the aspect.
 Aspect is expressed in positive degrees from 0 to 359.9, measured
clockwise from north.
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Surface Modeling Operations

16. Slope (Spatial Analyst)
 Identifies the slope (gradient, or rate of maximum change in z-value)
from each cell of a raster surface.
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Surface Modeling Operations

17. Contour (Spatial Analyst)
 Contours represent points having equal heights/ elevations with respect
to a particular datum such as Mean Sea Level (MSL)
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Surface Modeling Operations
 Topographic maps, shape of
a landscape represented with
contour lines
 A way to describe three-
dimensional landscapes in
two dimensions

18. Contour (Spatial Analyst)
 Contour tool: Creates a line feature class of contours (isolines) from a
raster surface.
 A base contour is used; e.g. to create contours every 10 meters, starting
at 5 meters
 5 is the base contour, and 10 is the contour interval. values to be
contoured 5, 15, 25, 35, 45, …
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Surface Modeling Operations

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Surface Modeling Operations
Hillshade (Spatial Analyst)
 Hillshade tool: Creates a shaded relief from a surface raster by considering
the illumination source angle and shadows.
 The hillshade raster has an integer value of 0 to 255, o representing

20. Viewshed (Spatial Analyst)
 Determines the raster surface locations visible to a set of observer
features
 Useful in application like telecommunication
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Surface Modeling Operations

21.  Spatial interpolation is used to take known values and
interpolate them into a surface, deriving new estimated
surface values
 Elevation points interpolated to generate DEM
 Contour lines interpolated to generate DEM
 Using interpolation methods, users can create surfaces
from sampled locations without having to visit every
location of a study area, saving time and effort.
Surface Modeling Operations

22. Interpolation(Spatial Analyst)
 Interpolation is creating a surface based on a sample of
values with the domain
 Different techniques for interpolation:
 IDW (Inverse Distance Weighting)
 Kriging
 Natural Neighbor
 Spline
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Surface Modeling Operations

23.  IDW: Interpolates a raster surface from points using an
inverse distance weighted (IDW) technique
 Values for nearby points tend to be more similar
 IDW weights the value of each point by its distance to the
cell being analyzed and averages the values
 It assumes that unknown value is influenced more by nearby
points than far away points
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Surface Modeling Operations

24.  Spline: Interpolates a raster surface from points using a two-
dimensional minimum curvature spline technique.
 It fits a curve through the sample data and assigns values to
other locations based on their location on the curve
 Regularized Method: results in a smoother surface that
smoother areas of abruptly changing values
 Tension Method: results in a rougher surface that more closely
adheres to abrupt changes in sample points
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Surface Modeling Operations

25.  Kriging Method: Interpolates a raster surface from points using
kriging.
 Like IDW interpolation, Kriging forms weights from surrounding
measured values to predict values at unmeasured locations.
 IDW uses a simple algorithm based on distance
 Kriging weights come from a semi-variogram that is developed by
looking at the spatial structure of the data
 Predictions are made for locations in the study area based on the
semi-variogram and the spatial arrangement of measured values that
are nearby
 Semi-variograms measure the strength of statistical correlation as a
function of distance; they quantify spatial autocorrelation
 Kriging associates probability with each prediction
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Surface Modeling Operations

26. Raster Calculator (Spatial Analyst)
 Builds and executes a single Map Algebra expression using
Python syntax in a calculator-like interface.
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Surface Modeling Operations

27. THANK YOU!
denekewa@Un.org